{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples and Exercises from Think Stats, 2nd Edition\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import nsfg\n",
    "import first\n",
    "import analytic\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential distribution\n",
    "\n",
    "Here's what the exponential CDF looks like with a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(3)\n",
    "for lam in [2.0, 1, 0.5]:\n",
    "    xs, ps = thinkstats2.RenderExpoCdf(lam, 0, 3.0, 50)\n",
    "    label = r'$\\lambda=%g$' % lam\n",
    "    thinkplot.Plot(xs, ps, label=label)\n",
    "    \n",
    "thinkplot.Config(title='Exponential CDF', xlabel='x', ylabel='CDF', \n",
    "                 loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of interarrival times from a dataset of birth times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = analytic.ReadBabyBoom()\n",
    "diffs = df.minutes.diff()\n",
    "cdf = thinkstats2.Cdf(diffs, label='actual')\n",
    "\n",
    "thinkplot.Cdf(cdf)\n",
    "thinkplot.Config(xlabel='Time between births (minutes)', ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the CCDF looks like on a log-y scale.  A straight line is consistent with an exponential distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdf(cdf, complement=True)\n",
    "thinkplot.Config(xlabel='Time between births (minutes)',\n",
    "                 ylabel='CCDF', yscale='log', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normal distribution\n",
    "\n",
    "Here's what the normal CDF looks like with a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(3)\n",
    "\n",
    "mus = [1.0, 2.0, 3.0]\n",
    "sigmas = [0.5, 0.4, 0.3]\n",
    "for mu, sigma in zip(mus, sigmas):\n",
    "    xs, ps = thinkstats2.RenderNormalCdf(mu=mu, sigma=sigma, \n",
    "                                               low=-1.0, high=4.0)\n",
    "    label = r'$\\mu=%g$, $\\sigma=%g$' % (mu, sigma)\n",
    "    thinkplot.Plot(xs, ps, label=label)\n",
    "\n",
    "thinkplot.Config(title='Normal CDF', xlabel='x', ylabel='CDF',\n",
    "                 loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll use a normal model to fit the distribution of birth weights from the NSFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "preg = nsfg.ReadFemPreg()\n",
    "weights = preg.totalwgt_lb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the observed CDF and the model.  The model fits the data well except in the left tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean, Var 7.280883100022579 1.5452125703544872\n",
      "Sigma 1.2430657948614334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# estimate parameters: trimming outliers yields a better fit\n",
    "mu, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "print('Mean, Var', mu, var)\n",
    "    \n",
    "# plot the model\n",
    "sigma = np.sqrt(var)\n",
    "print('Sigma', sigma)\n",
    "xs, ps = thinkstats2.RenderNormalCdf(mu, sigma, low=0, high=12.5)\n",
    "\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.6')\n",
    "\n",
    "# plot the data\n",
    "cdf = thinkstats2.Cdf(weights, label='data')\n",
    "\n",
    "thinkplot.PrePlot(1)\n",
    "thinkplot.Cdf(cdf) \n",
    "thinkplot.Config(title='Birth weights',\n",
    "                 xlabel='Birth weight (pounds)',\n",
    "                 ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A normal probability plot is a visual test for normality.  The following example shows that if the data are actually from a normal distribution, the plot is approximately straight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1000\n",
    "thinkplot.PrePlot(3) \n",
    "\n",
    "mus = [0, 1, 5]\n",
    "sigmas = [1, 1, 2]\n",
    "\n",
    "for mu, sigma in zip(mus, sigmas):\n",
    "    sample = np.random.normal(mu, sigma, n)\n",
    "    xs, ys = thinkstats2.NormalProbability(sample)\n",
    "    label = '$\\mu=%d$, $\\sigma=%d$' % (mu, sigma)\n",
    "    thinkplot.Plot(xs, ys, label=label)\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='standard normal sample',\n",
    "                 ylabel='sample values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the normal probability plot for birth weights, showing that the lightest babies are lighter than we expect from the normal mode, and the heaviest babies are heavier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "std = np.sqrt(var)\n",
    "\n",
    "xs = [-4, 4]\n",
    "fxs, fys = thinkstats2.FitLine(xs, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "xs, ys = thinkstats2.NormalProbability(weights)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Birth weight (lbs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we suspect that the deviation in the left tail is due to preterm babies, we can check by selecting only full term births."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_term = preg[preg.prglngth >= 37]\n",
    "term_weights = full_term.totalwgt_lb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the deviation in the left tail is almost gone, but the heaviest babies are still heavy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PR0Q46qij2pW6MMb038ZdjZ22lRS6+Oxp8zptX7vb26ckAHDMDE+/YxsMOUsEIlICfBvoValQVV0JrARYtmyZZjG0QeFyuYjH46nXAylJrarceuutnHXWWe22r1q1qtNiNIWFhx4nHQ5H6rXD4ehUs8gY03etwUhqdJDT4eDrH1uEQ4TZkyoYW96+OWe/L8hj6/anXh85uZyjp2WuQFpVVsD4svw1C0FunwjmALOAtqeBacBaEVmhqvszvrMH3TXf5FJ1dXWqaWft2rVs37691+8tLy+nubk59fqss87i17/+Naeddhput5vNmzczdWrfO6OMMQO3ZZ8v9fP08aWsmDehy+NUlYff2kcklrhvnVBewEVLJlPgGvqDM3OWCFR1HZD6DYrIDmCZqtZ3+6Zh5OMf/zh33XUXS5YsYfny5cyb1/mRsTuLFi3C5XKxePFirrjiCq699lp27NjB0Ucfjaoyfvx4a+s3Jk+efvNQHaH5Uyu7Pa6mKZhaXMbtFD51zNRhkQQARDU7rS4ich9wCjAOOADcpKq/Tdu/g14mgmXLlunq1avbbdu4cSMLFiwYzJBHDfvdGdM7B5oCfPFXL6ZqCf3088czc0J5l8f+4e19rNnlBRJt/h9bPDlncXZHRNao6rKejsvmqKFP9bC/OlvXNsaYwfDEG7tSSWBRdVW3SSAYifHOnkNNSMtndP/kMBQNj+cWY4zJMVXlpY2Hui/PPXZmt8e+s9eX6huYWF7AtDzVDOqvYZ0IstWsNZLZ78yY3tm2v5nGlkSbf1mxm8XVY7s9dnWySQhg2YzKvM4J6I9hmwiKiopoaGiwL7Y+UFUaGhooKhpedyvG5MPrm2tTPx89Z1yn2cNt9nqD7GlKDBd3OYQl0/I7J6A/hu2axdOmTaOmpoa6urp8hzKsFBUVMW3atHyHYcyQt3rLoe+WZXPHd3vcml2HJo8dObmckgJnVuPKhmGbCNxud7sZtsYYM1jqfUF2HEjM7XE6HCzpoowEQDgW5620TuJ8zxDur2HbNGSMMdmyJu1p4MgZYygtcnd53IZ9zQQjiYoCVaVuZlflr4LoQFgiMMaYDtbtOJj6OVOz0Fs1hzqJjxmGncRtLBEYY0wHzYFI6udp47peMCYSi7PzYCD1evHUzDWFhjJLBMYY00HbojMAhe6uO393NQZScwfGlRVQWdx189FwYInAGGM6CEUOVRIu6iYRbK07tLrYnDyuLjYYLBEYY0wH4eihJ4ICd9dfk1vqD603PKeb5qPhwhKBMcZ00FPTUCAcY683MYlMJL/rDQ+GYTuPwBhjBls4EuPJNbvx+sOpbV01DW1r8NNW1GBaZRHF3TQfDReWCIwxBli7tZ7fPPEuDWkLzpcUuigu6Pw12fY0AFCdxbkDqkosFsPlyu5XtSUCY8yop6r84rH1NAcOPQlMrCzmCx8+Aoej89yAnQcPdRRPyMIyk/F4nJaWFrxeL9FolOrq6qzOUbBEYIwZ9Xz+SCoJOB0OrjpzPmcsmYqri0Jzb+72sr0hMX/AIVBdVTxocUQiEbxeLz6fr90a6C0tLZSXd70WwmDIWiIQkduBjwK1qrowue2/gHOBMLAVuFJVm7o/izHGZN/37l+T+nnymGLOPmZ6l8ft9QZ5+K19qddLp3sYW1IwoGurKoFAgKamJvx+f5fHeL3erCaCbI4augM4u8O2Z4CFqroI2Axcn8XrG2NMj1SV3XWHhoJOH1/W7bFrdnvbvT517rh+XzcWi9HU1MSuXbvYu3dvt0kAwOl0tntCGGzZXKryBRGp7rDt6bSXrwIXZev6xhjTHVXlzZ2N/PqZzTQEowQKDt3V+4uK+K9nt3T5vpbQoWGlJ8wew5iSvs8mDoVCeL1empubM66n4nA4qKiowOPx4HZnd9ZyPvsIrgIeyOP1jTGjkKpy52u7eWztHlpD0cTGZEdsSaGLUEwJBaIZz+F2Ch86vPtidF1ds7W1Fa/XSyAQyHhsYWEhHo+HsrIyHI7cTPXKSyIQkW8DUeCeDMdcDVwNMGPGjBxFZowZ6WqagmzY13woCSSJJEYK9UQETj6sCnc3K5ali0aj+Hw+fD4f0Wj3yUVEKCsrw+PxUFhYmPMqpjlPBCJyOYlO5NM1w3ORqq4EVgIsW7bM1qM0xgyK1Tsa2bY/sZiMMx6nROP86NJjKC12dzlnoKNClyPjKmSqmmr+aWlpydj843Q68Xg8VFRUZH2uQCY5vbKInA18C/igqnbfM2KMMVkQisS4/+UdtIQTbf2uaJR5k8uZO3ngJaTTx/6HQqGMxxYXF+PxeCgtLR0Saxhkc/jofcApwDgRqQFuIjFKqBB4JvnhX1XVL2QrBmOMaVPvC3LDna/TFD30xeuIxzl6Tv9H/kBi7L/P58Pr9WYc2SMilJeXp5p/hpJsjhr6VBebf5ut6xljTHfqfUE+/z//IFRYCMk8UFzg4lvnLOa4eb3v9G3TNvbf6/XS2tqa8Vi3243H46G8vBync2jWJLKZxcaYEUtVefqtPdz67PtEiopS22eOL2PupHKOmze+T00z8Xg8dfcfiUQyHltSUoLH46GkpGRINP9kYonAGDNivburkV8+s5lI2jj8imI3xx9WxWnzxvX6CzocDqdKPwyVsf+DyRKBMWbE2rrfRyytOaakwMUPLjySeRN7LtfQl7H/BQUFVFZW5nTs/2CyRGCMGZF8/jCPvbaTePKuv7jAxdfOmttjEojFYqnmn0xj/4HU2P+ioqIh3/yTiSUCY8yIU9sU4J9/9Q8iTieaLB8xsbKIORnqCAWDwWE19n8wjYxPYYwxSc2BMN+64zViIoTSagh5Sgqo7FAbSFVTY/+DwWDHU7VTVFSUKv0wnO/+u2KJwBgzIjzy8nb+8Mp2GnESFweaNkpowVQP13ygOrWkZDQaTXX+xmKx7k45pMf+DyZLBMaYYS0cifHChn3cs+p9ggUFxNJqAI2vKGLG+DKuOWkmM8YUp+r+9zT23+VypZp/hurY/8HUYyIQkQnAicAUIACsB1aravaKYxtjTA9agxGee3sPdz63mZgIwaIiNNlkI8CYskJmTyrnhFljqHRG2L27nnA4nPGcw2ns/2DqNhGIyKnAdcBY4E2gFigCLgDmiMjDwE9U1ZeLQI0xJt23f/8Gu+taiIsQSGsGmj2pnAkVRXzl5JmE/c20trZQV9fc7XkcDkeq+aegYGCrjQ1XmZ4IzgH+SVV3ddwhIi4SFUTPBB7JUmzGGNNOMBLj189v5W8ba4nE4pD2FABQPaEMT7GLC+YW03BgT8ZzFRQUpEo/DMex/4Op20Sgqt/IsC8K/CkrERljTBfe3O3lp89sptabHN2TlgBmjCulvMhJRYHysXkFlDi6L/9QWlpKZWXlsB/7P5h600dwLfA7oBm4DVgKXNdh2UljjMmKDXu8PPbmHtbW+PAF2n/BqyrTq4rxFMKxk10sndT1yB6n05kq/TBSxv4Ppt78Rq5S1Z+LyFnAeOBKEonBEoExJqsa/RFufPTd9quJKUgsyvmHV3DC7HJcTsHlAKej8939SB77P5h6kwjafnvnAL9T1bfFfqPGmCxrDoT58v3vHEoCqsRVIRrlQzMKOXOBp8v3pS/7WJTWiWy615tEsEZEngZmAdeLSDlgQ0eNMVmx3xvk189u5tVdXlBQFI0rqnFOmFLIksnlHDW9c72g0Tb2fzD1JhF8DlgCbFNVv4hUkWgeMsaYQRGKxnhnTzOr3qvl2Q0HEglA46hqqu7PF5dXctS0zgmguLiYysrKUTf2fzD1mAhUNS4i1cBlIqLAi6r6x57eJyK3kxhiWquqC5PbxgIPANXADuBiVW3sb/DGmOHvqXX7ufv13bQEIwRCUeKaeAKAQ4Xfzp9T3C4JiEiq83e0jv0fTL0ZNfQr4DDgvuSma0TkDFX9Yg9vvQP4JXBX2rbrgOdU9RYRuS75+lt9jtoYM2ypKsFonKbWMLf89X027fEm7/zj7ap+SiTC2QsqOf+osaltbrebyspKG/s/yHrTNPRBYKEm/4ZE5E5gXU9vUtUXkk8S6c4nsaA9wJ3AKiwRGDMqeAMR3q9r5Z7Xd/Pu7iZINvvEVSEtATgCAaaWOvjiR2ZSWZqoFlpaWorH46G4uNiaf7KgN4ngPWAGsDP5ejrwTj+vN1FV9wGo6r5kHaMuicjVwNUAM2bM6OfljDH5dtAf5vH1B1i7s4nd9S00ByKJJ4AOzT/uUJArVoxnUuV4po4twuFwpDp/h9Oyj8NRplpDfybxt+QBNorI68nXxwIvZzswVV0JrARYtmxZ96tEGGOGJFXlhS0H+evGWrYd8HHQF+rU/IMqLoFTphVw0fLpABQWFqbG/lvzT25keiL4cRaud0BEJiefBiaTKGRnjBlB4qpsqWvljld34/OHeW9PU+LLvy0BxOM4wmFOnlnK+csmUlzgbDf2v7Cw0Jp/cixTraG/Z+F6jwGXA7ck/3w0C9cwxuRYMBKjtjnMur1enn63lh21zfiD0eTd/6EnAGdLC/PHF/H5c2dTUuDE5XJRUVExopZ9HI4yNQ01k/43mLYLUFWtyHRiEbmPRMfwOBGpAW4ikQAeFJHPAbuAT/QzbmNMnqkq2xr8bK5t5ZmNtdR7g+xvbG039r+No7UViUT4yodnM3dSKcXFxXg8HkpLS+3ufwjI9ETQeeZGH6jqp7rZdfpAzmuMya9AOMYrOxp5asMB9jcGaAlGaA6EO3X+Akg4jNMfYGl1BVd8cDpjKj0jftnH4SjTE0GZqrZkenNvjjHGjBwHW8P85G/bqPUG2FnbnCr9kE6iUSQc5pgpxVy4fDoTx5Sm6v5b6YehKVOj3KMi8haJdvw1qtoKICKzgVOBi4H/Ax7OepTGmLw64Avx21d34fWHeXNrfaex/21f/uWOOOcfM5Hj545J1f23sf9DX6amodNF5BzgGuBEERkDREnMK/gLcLmq7s9NmMaYfPAFI/zs+e0EwhFqm/zsrG0lvfnH4fdT5oQLjh7P5MoiZk9M3P17PB4b+z+MZOymV9UngCdyFIsxZoh470ALG/b5eHVbAwea/OxvDBCLt38CcLS0cNGKyZx65Dgb+z/M2XgtY0zKgeYQ975Rw76mVuqaAuxu8LfbL+EwDr+fQpeDmy9ZwOTxlam6/9b8M3xZIjBmlPMGIoRjce59bRdbapuJxmLsrPMTjrbvBHb6fEg8zkePnsSnTplnyz6OIPa3aMwoFIsrW+tbuW/1Hnz+EHsaWqj3hTodJ5EIEg4jkQgfP3Y6py6ZwWHTx9vd/wjTmzLUv1fVz/S0zRgz9O33BVm3t5nnN9dR7/XjbQ3T0NxFAojHcTQ3IwrnLp/OZ89cQHlpSR4iNrnQmyeCI9NfiIgTOCY74RhjBltLKMozm+o44Auxrb6Fmrpmar0hOk3+iscTdYD8fiZXFDK+ehz//JGFzJ5cmZ/ATc5kmlB2PXADUCwivrbNQJhkVVBjzNBV3xLm/jV72OcLEgxFeH+vF3/bQvBpJBTCEQoh8ThXnTaHjxw3x5Z9HGUyzSP4EfAjEfmRql6fw5iMMf0UV2XnwQC3vbyLWDyGtyVIgy/IwZZw+wNVcQSDSDjM8lmVHH/ELE5ZPJPSkqL8BG7yqjdrFl8vIlOBmenHq+oL2QzMGNN7Da1h1u9t5ulNtTT7QxxsDrK/MdDpOGdLC8TjSDzOv5w1hyVzJzNtYpWN/R/letNZfAtwCfAuEEtuVsASgTF5pKq8sPUgOxr8vHeghf2NLdR7g52afyQaTVT/VGVsWQHXf+wo5kyfYKUfTEpvOosvBOarauehBcaYnGsJRXnozb1sqfMTiUbxtgbZuq+Fjp2/jlAICQYRVZbOquTkhVM5c9lsG/tvOunN/xHbADdgicCYPKptDvHLF7YTiyvBUIQt+3y0BiOdjnMEAkg4TGWxi7NPmM7pS2cyfVKV3f2bbmUaNXQriVsMP/CWiDxHWjJQ1S9nPzxjRjdVZXNtK4+t309ja5hAMMzeg/5OY/8dgQASjUIsxocXT+CkhVNYOHsyRUXW+Wt6lumJYHXyzzUklpg0xuTQpgMt3L9mD8FwlGg0QmNLiJ11h2r/SCyGw++HWIzyIhcL51TyL+ccSdXYSqv7b/ooYLeEAAAgAElEQVQk0/DRO7N1URH5N+DzJJ441gFXqmowW9czZriIxZW/bqxl9a4mWoNhIpEIDc0hDnjDRGOJ2j8SieDw+xFVClwOvvHxIzh6/jQb+2/6rTejhtbRee1iL4knhu+rakNfLpgcivpl4AhVDYjIgyRGJd3Rl/MYMxKEo3H2eoMo8GaNlzd2NNLiDxEKJwrB1TQcGgIq4XCiAzgWA4Rrzz2C05ZW27KPZsB601n8JIlho/cmX19CYoaxl8SX97n9vG6xiESAEmBvP85hzLAViMRYs6uJJ9+tA5RYLMZBX4BtB9ov/ALtnwAcDgcfXDyNf7twKQVuG/1jBkdv/k86UVVPTHu9TkReUtUTReSyvl5QVfeIyI+BXUAAeFpVn+54nIhcDVwNMGPGjL5expgh6YAvxOu7mnh1eyOqSjQapa7J36nuP6qJL/9oFFHF6XRx2alzOfe42ZQVF+QneDNi9SYRlInIsar6GoCIrADKkvs6Fy7pQXLJy/OBWUAT8JCIXKaqd6cfp6orSdY0WrZsWcemKWOGDVUlEInz2Lr9rNvbTDweIxKJUtvkZ2/joa4xiSb+OUkwiCMaZc7EMoqKKlg8ZwKfOW2+tf+brOlNIvg8cLuIlJFoEvIBnxeRUuBH/bjmGcB2Va0DEJE/ACcAd2d8lzHDSDga50BziNd2NPJmjY9YPE5rIEwgFGZHrZ+O6/5KOEzb1/yJh4/n4pPnMnf6eCv9YHKiN7WG3gCOEhEPIKralLb7wX5ccxdwnIiUkGgaOp1DQ1WNGdb84Riv7Wjk2ffqAYhEYxxobGXPQT9o2oNtW9NPJJJMAMKHj57Kx06ay/SJVvbZ5FamCWWXqerdIvLVDtsBUNWf9ueCqvqaiDwMrCXRtPQmVtbaDHOxuPLajkb+sqEWUILhCPsP+jnQ1KHwmyrO5uZE7X/A4XBw8Umz+cTJ8ygtttE/Jj8yPRGUJv8sH+yLqupNwE2DfV5jcml7g59V79cTiMTZ0xSk1uun3hsgFou3K/wm0Wii5HOyD2DGuGKOmD6Gz511BGVlZdb2b/Iu04Sy/03+eXPuwjFm6PIFI/jDMfZ6Qzy2bj+BcIxILI63NcSuuhY0rmRq+//k8VM5Z3k1Y8ZUUlBgI3/M0NGbCWXzgF8DE1V1oYgsAs5T1e9nPTpj8kxVqWkK8uf1B9jTlBjhE4rE2F3fQmNLCI3HUe1Q9bNDArjmzNmcuHA6VWM8VvrBDEm9GTX0f8A3gLYnhHdE5F7AEoEZsRr9EdbsbmL1Li/NwUSTTmNLiHpfgKaWEHHVVOevRCI4gslhoLEYH1s+mTkTS/CUl3LErElW+sEMeb1JBCWq+nqH/5H7PH/AmOFgc20Lr2xvZHNta2pbrTfArtpm4qqJ5p94DFFNjPgJBikpcFLlKeC4uZWceuR4PB4PHo8Ht9udx09iTO/1JhHUi8gcko2fInIRsC+rURmTI6rKe7Wt/OntfQSjcSKxxF1+JBZj8x4v/mAU1UTzj0SjOAOBZK0fKCl08dkzZrJwegWFhYV4PB7Kysps7L8ZdnqTCL5IYnjn4SKyB9gOXJrVqIzJElVlV2MAXzBKOBrnD2/vT233BSLE4nHqmgJ4/eFE278qjkAARzSaSgDHzx3DsjmVzJ9cRllZGZWVlRQWFlrzjxm2ejOhbBtwRnImsUNVm7MfljGDzxuI8PNV2wlF4+22B8JRNuxsRFE02fwj4RDOUAhisVSn79mLJ3DKEVV4ShN3/xUVFbbsoxkRejNqaCvwKvAPEgvWv5vtoIwZTA2tYZ7fXM+bNb52233+MAea/DS1hBJ3/8EgjkBiAljbl//siaVcfvI0KkvclJWW4PF4KC0ttbt/M6L05nbmCOBY4APAj0XkcOBtVb0wq5EZMwCRWJz9vhB/eHsftc3htD1KY0uYXXXNRAIh4rFYYqWvYDD15b+02oPb6eD4eWOYN7mM8vJyPB6P1f03I1ZvEkEMiCT/jAMHgNpsBmVMf7SN+X97j49Xtjd23Iu3NczmvV40rji8TUg8Tvqo/pJCF9/52FzKily43W48Hg/l5eU29t+MeL1JBD4Sy0n+FPi/vq5IZky2xVV5d18z963pan2jZALY04TG4jhampG0dn+AJTM9nLV4PNPGFlFaWorH47Gx/2ZU6U0i+BRwEvAvJMpPvwy8oKrPZTUyY3qw3xfkpW2NrN3t7XJ/ndfPztpm4sEwDn8rjg4zgD+4oIrzjplEcaGLiooKG/tvRq3ejBp6FHg02TfwYeArwDeB4izHZkxK2+IuOw/6eXpTHeFonKZA+3mNcVUi0Rjb9vto8fkhFMIRDtOxYefMo8bz4SUTKCspSjX/2Nh/M5r1ZtTQI8ASYAuJkUOfBV7LclzGAIkRP7sOBnj4re7nMEZjcWoaWqhtCiDBYGKFr3i803Er5ozh3GMmMn3iGDweD0VFRdb8Ywy9axq6BVirqrFsB2MMJJp8ttS18tYeH/u8oW6OUrz+CA3eAA1eP7S2JiZ9aedVTc88ajynHjmemVPG4fF4bOy/MR30doUyY7IqGInxwpaD7Gr0s70hkOFIpbE5xK49B4k1txKPxzq1/QNMG1vMFR+czpRx5YyvGmN1/43JwG6NTM6pKpG4Eosrj63bz15viPqWcMbjCxywZ5+XfXvqiUWjqdLP6V/tTodwxQenc/SsShv7b0wf5CURiEglcBuwkEQxu6tU9ZV8xGJyJxyNs7W+lbvf2NOr4wtdwv79XnbvbSQeChOLdV30dvHMCpbPruSYOWOprKykoqLCxv4b0we9SgQiMhWYmX68qr4wgOv+HHhKVS8SkQKgZADnMkNMMBJj4/4WAtFD3UrNwSgvbDmY8X2xeJym1jCLJ5RSIMqfXtxCLBoh3kXHLyRq/xw/bwzTJ1Ta2H9jBqA3o4b+H/BJEjWG2v5lK4m6Q30mIhXAycAVAKoaBrpvFzDDys6Dfla+tKvXx7udQpHbyZlzx/L9+9agsRg170WJRqOkL/sI4HI6mD2hhE8eP4Wq8kKqxiTq/tuyj8YMTG+eCC4A5qtqd8M3+mo2UAf8TkQWA2uAa1W1Nf0gEbkauBpgxowZg3Rpky0toSiPvrOfd/e39Or442eN4UOHj6fA5WDdjgb+/a7XiEYjxGJdD0772kfmMGtCCQUFBTb235hB1ptEsA1wA4OVCFzA0cCXVPU1Efk5cB1wY/pBqrqSxDoILFu2rPOwEDMk+IIRfvWPnanlHDs6btaYdq8nlhdwzPRKnA4hFotx2xNv88jLO1Bt3/xz8oIqAMaVF3DivLGMrSynsrLSxv4bkwXdJgIRuZXEs7kfeEtEniMtGajql/t5zRqgRlXbJqU9TCIRmGGkriXEk+/W8t6B1i73z6oq5rMrplPg6nzXHgqF2La3jp/9+V22dvH+n1x2JIVuB06nM1X6wcb+G5M9mf51rU7+uQZ4rMO+ft+hq+p+EdktIvNV9T3gdGyNg2Flw75m7l3d9cifIpeDa06ayYTy9sM2VZWWlha8Xi/v7mzgp09s6/TeE+eP5ZLjp1BcXJxa9tHu/o3Jvm4TgareCSAi16rqz9P3ici1A7zul4B7kiOGtgFXDvB8Jkf+saWBpzbWddo+c2wxlxwzhYqi9kXbotEoPp8Pr9dLLBbj1fcbufvFmk7vv/KUGZyyaFqq9IMxJnd687x9OYnhnumu6GJbr6nqW8Cy/r7f5FYsrmyubely/H95kYuPLZ7EvAllqW2qSjAYxOv10tKS6Dz2+iN856H3iMXbP0yKOPjOJxez4ojpNvbfmDzJ1EfwKeDTwGwRSW8aKgdsTYIRTFXZcTCANxAhFI3z2LoDXR531JRyLjlmaup1PB6npaWFpqYmwuHE4u81B4Nsr/Xz4Kvt1wpwOJ3MnzqG//zcCbhdlgCMyadMTwQvA/uAccBP0rY3A+9kMyiTP5trW7jztc5NNx1dtHQyS6d5AIhEIni9Xnw+X2ryVzSmrHxuJ+/uaU57l+ByuXC7XVxxxuGcf1x1Fj6BMaavMvUR7BSRGqBVVf+ew5hMHtQ0BXhw7V4aWiMZjzt+1hg+cuQEAFpbW/F6vfj9/nbHbDvQ2q4zWMSB2+3C5XIjAnd99VRKi2wBGGOGiox9BKoaExG/iHhUtetloMywFY7FeWZTHWt3eQlGuy7jMH9iKUUuJxPKCzhh9licKF6vF6/XSyTSPmn4wzH++Po+Xnk/sV6w05m4+3c4nMyd4mHF/Amcf+xMXE6bCGbMUNKbzuIgsE5EngFSg74HMI/A5FlzMMo/tjbw0raOC7wfMm9CKR9dOJGq0kT5hlAoRFNDPc3NzanKn+me31DPI6/vAwS3243L5cbhSAz9/N6lyzhy5tisfBZjzMD1JhH8JfmfGWZUlbf3+KhLK/HsC0a7XeMXYMGkMs5dOBFPsbvd2P9AoPMaAeFonNXbmtjdEOSlzY0UFBTicrloG/q/qLqKb120mKICmwxmzFDWm4Vp7sxFIGZwba1r5fZXd/f6+IuWTmbh5HLcTgfRaJSDBw/i8/mSxd+69qtndrKjPojL5aaoqDiVAKrKi/jSuQs5qtqeAowZDjINH31QVS8WkXV0MZNYVRdlNTLTb9sb/L1OAhcsmsTymZWoKqFQiIa6JlpbW7ts/mnjcrkoKytnj3drp4Vfls4ex79fcvSA4jfG5FamJ4K22cMfzUUgZnBEYnFue7lzGegTZo2hpODQeP0J5YUcPrEMQVMzf0OhzHUF20o/lJaW8pM/vpPqAwC48PhZzJpUznHzJgzehzHG5ESm4aP7kn/ubNsmIuOABs10u2jypr4lzH8/376Gz2RPIZ87bgbFBe0nbUUiERoPNrQb+98VEem07OMfXt7OK5vaTzK77NS5g/QpjDG5lqlp6DjgFuAg8B/A70lMLnOIyGdV9anchGh6UtcS4rn36lm3t7nTvn89eVbqZ1UlEAjg9Xppbe26amgbt9udqvufXvrh9c213LPq/XbHfv8zywf4CYwx+ZSpaeiXwA2AB/gb8GFVfVVEDgfuAywRDAGRWJw7XttNk79zp+71HzoMSJR+aGv+6Tj2v6OSkhIqKyspLi7uVPmzJRDh/z38Vrttl58+jwXT2685YIwZXjIlApeqPg0gIt9T1VcBVHWTlQYeGuKq3P5q5yRQWuDkmpNmUiBx6urq8Pl8GTt/HQ5Hqu6/2931jN/mQJgr/ntVu22nLZ7KecdWD/RjGGPyLFMiSG847jiI3PoI8kRV2XSghYbWME9trCP9+32yp5DzFk5kjDuGr6mOxi7G/qcrKCigsrKSsrKyjMs+HmwO8U+3tq8yMmVsKf9yzhED+izGmKEhUyJYLCI+QIDi5M8kX1vB+Dzwh2P88oXteAOdm4GK3cLHF1QQ8tVyIMPYf4CysrJU3f+enu6C4WinJADwi2tOsEVjjBkhMo0astrAQ0hclac31nZKAvF4jEgkyqXzSmn1dl8ywul04vF4qKio6NOyj398ZUenbfd+43RLAsaMIDb3f4iLxZW/ba7nle2NhFKF4ZRoNMbhY524VJkzpoiCbtJ2UVFRv5d9fHHDPh5+qf1w1F9ccyKFbrtHMGYkyVsiEBEniXWR96iqTVpL0+iP8H5dC6qwfl8z2+oTZZ5V40QiUaLRKJcdWYqnyAl07tztaux/X9353GYee21Hu223fuFEpowt7df5jDFDVz6fCK4FNgIVeYwhb2Jx5Y2dTezzBdttD0bjrG83H0CJxeJEoxHisRhup3D81MJkEmjP7XZTUVFBRUVFv5d93FTTxG1/3cT2A75224+bP9GSgDEjVF4SgYhMAz4C/AD4aj5iyLe39/j48/qul4BMUKLRKJFIlHg8zoophSyfXNpl805JSQkej4eSkpIBtd03toT49l2vd9p+4fGzbOawMSNYvp4IfgZ8k8T6x10SkauBqwFmzJiRo7ByZ5832OX2eDxx9x+NRlGFJRMLmF3pZkp5+7+qtrH/FRUVFBQUDEpMn/9F59FBV5wxn3NXzByU8xtjhqacJwIR+ShQq6prROSU7o5T1ZXASoBly5aNuHkL/kgs9fOSaRVMKnHQ0tpCOBQG3FQUFjKt3NnpDr+goCBV+iHT2P+++s7dqztt+8nnjqd6Yre52hgzQuTjieBE4DwROYfEfIQKEblbVS/LQyx5sb3Bzzt7ErN9o9EI46WVyU5J9pZ0fXffl7H/ffXihn1s2HWw3bb7vnE6BTY6yJhRIeeJQFWvB64HSD4RfH00JYG4Kg+uqSEQDBGLRRlf4mRySdfHOp3OVOmHvoz97wtV5Zd/ebfdtp9+/nhLAsaMIjaPIEfaln3cVFPPvoOJUUEFTuGcOSU4He3v8Acy9r+vwtE4keihZqp/u2ARMydYc5Axo0leE4GqrgJW5TOGbItGo3i9Xnw+H7FYjIaWQ9U/p5Y7KStItPOLSLvmn1x59q097V6fdMSknF3bGDM02BNBFqgqwWAQr9dLS0tLu32+0KFafsUuBy6XK1X6ob9j//vD5w/zu2ff44X1+1Lb5k+rzNn1jTFDhyWCQRSPx2lubsbr9RIOhzvtD8eUl2sSw0adTgeTxlUyc+a0vNTtufJnqzptW3bY+JzHYYzJP0sEgyASiaSaf7pb9jEaV57cGsDtduFyuXE4HBw+ZUxeksCTqzuvaXziEZO48PjqnMdijMk/SwT9pKr4/X68Xi9+vz/jsU6Xm+d3RTgYc1NQkPjiP2VuFXPH575kQyQa57anN7XbdvOly1g4c2zOYzHGDA2WCPooFoulmn96WvaxtLSUiooKntzsZYc3QGIpB/jQgvF88LCqHETb2Wd++ny71/92wSJLAsaMcpYIeikUCuH1emlubs647KM3DBsaIe5w4zgYJRxtYHvDoSeGDx5WlfMkEI7EuP+Frfz59Z3E02KfVlVqo4SMMZYIMlFVWltbaWpqIhjsujZQm8LCQjweD6vWN7L9YADovErY0dM9nHn4uCxF27VwJMY1//MPfP7Ondf/dsGinMZijBmaLBF0IRqN4vP58Hq9xGKxbo/raux/fUvXFUVnjC3m/KMm5rxz+I7nNneZBL536TKrI2SMASwRpKSP/W9tbc3Y/ONyuVKlHzqO/Q/FDo0auuSYKbgdgsvpYObYYlzOwSsS1xsHm0P8de3udtuu+8RSZk8qp6rclp02xiSM+kQQj8dpaWnB6/USCoUyHltcXIzH46G0tOt1AVSVcPRQAlk4uTyva/t2XHT+WxctYflcmytgjGlv1CaC3oz9h0TzT9vdf091/w+tKQwFLslrEojHOz/RrJg3IQ+RGGOGulGVCFSVQCCQav7JxO12p+r+97b0Q3oiKHTlt3rn7vr2pS3u+fppeYrEGDPUjYpE0Nex/x6Ph+Li4j7f0Ydj6Ykgt/0B6WKxOF+97ZXU6yljSykqGBV/1caYfhjR3w69Hfvftuyjx+PB7Xb3/3rRoZEIvv/Am+1eTxnbzYIHxhjDCE0EoVCI+vp6AoFAxuPaxv6XlZUNyrKP7foIcjxCqM1vn97EOzsaUq+LC1xce/5ReYnFGDM8jMhE4HA4uk0CIkJpaSmVlZUUFhYOaoduOI9PBK3BCDfc+To1De37Pv7vSydTXDgi/5qNMYNkRH5DuN1uSktL23UIO53OVN3/bC37GMxTIti4u5F///0bnbbfcsWxlgSMMT3K+beEiEwH7gImAXFgpar+fLCv4/F4aG1t7XHs/2AKtxs+mrtE0FUS+OqFi5g7xZOzGIwxw1c+bhejwNdUda2IlANrROQZVX23pzf2RXFxMTNmzOhx7P9gymVncUsgwh3Pvcfz7+xtt72k0MV3P72MOZMrsnp9Y8zIkfNEoKr7gH3Jn5tFZCMwFRjURCAiOU0CkNtE8NTa3Z2SAMDvv2bzBYwxfZO/MY6AiFQDS4HXuth3tYisFpHVdXV1uQ6tX3LVWRyNxbnv71s6bb/+E0uzdk1jzMiVt55EESkDHgG+oqq+jvtVdSWwEmDZsmXdTwIYQnLVWbzyqY3tXl9++jxOOWoKFSW5fQIyxowMeUkEIuImkQTuUdU/5COGbAjnYB7B6vfreO7tPe22nbtiZl7rGhljhrd8jBoS4LfARlX9aa6vn03Z6iPw+cP8x/1r2XvQTzDcfsGbr164yJKAMWZA8vFEcCLwGWCdiLyV3HaDqj6Rh1gGVWiQaw2pKg++uI0H/7G1y/1fOnchJxw+ccDXMcaMbvkYNfQibau4jzCDOY8gGovzqyfe5e/rOo8Mqigp4BsfW8wRM8YM6BrGGAMjdGZxvgSjh5a17O8Twf5GPw+9uI1VXSSAOZMr+PbFR1Ne7MbhGJG51BiTB5YIBtFAho/+Y8M+7vv7Fg40dV0j6fpPLGWZrS5mjMkCSwSDqL+dxS+s38fPH1vX5T6nw8H//POJjPcUDzg+Y4zpiiWCQRKLK219xQ4BVy+abt7e3sDq9+t4YvWuTvsqSgr40rkLmTulgvJimx9gjMkeSwSDJNSho7irIZ07DjTz59d30hqMsKnGS3Mg3OmY7126jOqJ5ZQW9X+BHGOM6QtLBINkw77m1M9dNQtFY3G+/fs3Os0DSHfN2Udw5MyxWYnPGGO6Y4lgEGza38xj6/anXs8bX9pufzQW53v3re02CZx4xCSWHTaeDxw5KatxGmNMVywRDNDOg37uX7uXeLIa0mRPIWcfMaHdMS+9u58Nuw6mXhe4nHzl/KMQgdmTKhhXUZTLkI0xph1LBANwoDnE71+vIRJLZIGxJW4uXzGdIrczdYyq8sdXdqRen7Z4Kv9yzhFWFsIYM2RYIugnbyDCna/tJhBJdBKXFjq54rjplBe5CEditJVLXbu1nt31LQAUup1cfto8SwLGmCHFEkE/+MMx7nhtN95Aos1fUOZ7Crj3b5vZsKuROm/Xk8LOOno6ZcU2GsgYM7RYIuglXzDCewda2XSgha31ranmoGA4yp6d9Wxc13koaDqnw8G5K2bmIlRjjOkTSwTdUFX2+UJsOtDCpgMt1DQGiMTiRKLxdn9GfX4iwfZJwOEQnI5DQ0iLC5x88gNzGFtemOuPYYwxPRr1iSAciXGwJURTa5hab5D3a1vY3uBnT3OI1nCccDROJBYjFlPSl0lzxOMURKO4YolCcyWFLi48fhYLZ45h9qQKXFlamMYYYwbbqE0EqspP/vgOL206QMzpTPzncKDddeSq4ozHccbjuGIxHHooLThE+Mr5R3HMYVYUzhgz/Iy6RNDW5PO3jQf42/YmYsXdF3MTVZyxGK54HFcsTkWJmzFlxYwtK6SyrCD5ZyELplUya1JFDj+FMcYMnnytWXw28HPACdymqrdk83qxuLKlLtHR+15tC95AlNqmALFkO77TIRS5nbhdDioKXUzzFHJYVQmHTSyjqqyQMeWFeEoKrLnHGDMi5WPNYifwP8CZQA3whog8pqrvZuuaqsr9a/cQjh5qzvGHIm07WTStko8umcr8iaWML7MOXWPM6JKPW9wVwBZV3aaqYeB+4PxsXtDldDB3fFnqdZHbgSMaoygcpjQY5LJlUzlpzlhLAsaYUSkfTUNTgd1pr2uAY7N90aXTKqgsdnH4xDJmji2hpn4y2/Y3s22/j1kTy7N9eWOMGbLykQi6GpajnQ4SuRq4GmDGjBkDvuiCSeUsmHToC3/mhHJmTijn1EVTBnxuY4wZzvLRNFQDTE97PQ3otFK7qq5U1WWqumz8eBuWaYwx2ZKPRPAGMFdEZolIAXAJ8Fge4jDGGEMemoZUNSoi/wr8lcTw0dtVdUOu4zDGGJOQl3kEqvoE8EQ+rm2MMaY9myFljDGjnCUCY4wZ5SwRGGPMKCeqnYbwDzkiUgfs7OfbxwH1gxjOYLG4+sbi6huLq2+GalwwsNhmqmqP4++HRSIYCBFZrarL8h1HRxZX31hcfWNx9c1QjQtyE5s1DRljzChnicAYY0a50ZAIVuY7gG5YXH1jcfWNxdU3QzUuyEFsI76PwBhjTGaj4YnAGGNMBpYIjDFmlBtViUBEvi4iKiLj8h0LgIj8h4i8IyJvicjTIjIkFkcQkf8SkU3J2P4oIpX5jglARD4hIhtEJC4ieR/qJyJni8h7IrJFRK7LdzwAInK7iNSKyPp8x5JORKaLyPMisjH5d3htvmMCEJEiEXldRN5OxnVzvmNKJyJOEXlTRB7P5nVGTSIQkekk1knele9Y0vyXqi5S1SXA48B38h1Q0jPAQlVdBGwGrs9zPG3WAx8DXsh3IGlrb38YOAL4lIgckd+oALgDODvfQXQhCnxNVRcAxwFfHCK/rxBwmqouBpYAZ4vIcXmOKd21wMZsX2TUJALgv4Fv0sVqaPmiqr60l6UMkdhU9WlVjSZfvkpi8aC8U9WNqvpevuNIyvna272hqi8AB/MdR0equk9V1yZ/bibx5TY1v1GBJrQkX7qT/w2Jf4ciMg34CHBbtq81KhKBiJwH7FHVt/MdS0ci8gMR2Q1cytB5Ikh3FfBkvoMYgrpaezvvX2zDgYhUA0uB1/IbSUKy+eUtoBZ4RlWHRFzAz0jcvMazfaG8rEeQDSLyLDCpi13fBm4APpTbiBIyxaWqj6rqt4Fvi8j1wL8CNw2FuJLHfJvEI/09uYipt3ENEb1ae9u0JyJlwCPAVzo8EeeNqsaAJcm+sD+KyEJVzWsfi4h8FKhV1TUickq2rzdiEoGqntHVdhE5CpgFvC0ikGjmWCsiK1R1f77i6sK9wF/IUSLoKS4RuRz4KHC65nCySR9+X/nWq7W3zSEi4iaRBO5R1T/kO56OVLVJRFaR6GPJd2f7icB5InIOUARUiMjdqnpZNi424puGVHWdqk5Q1WpVrSbxD/joXCSBnojI3LSX5wGb8hVLOhE5G/gWcJ6q+vMdzxBla2/3gSTuwn4LbFTVn+Y7njYiMr5tVJyIFANnMAT+Harq9ao6LfmddROgcSEAAAaPSURBVAnwt2wlARgFiWCIu0VE1ovIOySarobEkDrgl0A58ExyaOtv8h0QgIhcKCI1wPHAX0Tkr/mKJdmZ3rb29kbgwaGw9raI3Ae8AswXkRoR+Vy+Y0o6EfgMcFry/6m3kne7+TYZeD75b/ANEn0EWR2qORRZiQljjBnl7InAGGNGOUsExhgzylkiMMaYUc4SgTHGjHKWCIwxZpSzRGCAxCziZPXFtmqoxya3f0VESgbxOjsGUv1VRE7pTSVGEVnV3wqlIvJETxVXReSGDq9f7s+1ehlPoYg8m/x7+WS2rmNGL0sEBhE5nsQs4qOTFUfP4FAdna8Ag5YI+hGbM9fXVNVzVLWph8PaJQJVPSGLIS0F3Kq6RFUfSN+Rj9+PGXksERhITKqpV9UQgKrWq+peEfkyMIXEhJvnAUTk1yKyumPt9uSd/s0islZE1onI4cntVcm1Ft4Ukf8lrUaPiPxJRNYkz3V12vYWEfmeiLwGHJ+s+79JRF4kUYa6ExEpFpH7k080DwD/v72zDbGqCOP4758LZpqmrokfQsUyKbElU4istEKQiKJIiV60D71nUYR9EaGIUAqKggyTMlBEiyTMMjHYlaRS01UzMkMNgl4MxLLQ1t2nD89z2LPXe9ftRVq6zw8OZ87MnDPPzL13Zs6ce/5Pv1LadEmfhG1vSRogaYak1aU8UyWtLdWlsZaNkhYC/WKGvqKwOfaS+3P4ItphVun6zZLejrqsiLdtkbRQ0pdh+/MV9ToXWI5r4bRKGhP2LYj2uFVSk6RP1ek/YnCc2yzpBUmb5H4AJkl6R9I+Sc/UaMejkhZFnTdKmhzX2S8XbyxE2p6TtDXKvC/iB0j6qPQduDHiR0X5r0U7bpC/xZv0FswstzrfgAFAK+574BXg6lLaQaCxdDwk9n2AZmBCKd/cCD8ILI3wS8CCCF+PC7M1VlyrH67tMjSODZgZ4TPxu5ML8EFkNfBelTo8Drwe4Qm4WN5lQCPuv6B/pD2Jq7w24L4pivjFwB2Vde7GxqMV5R+N/S24P4c+wPAoYwQwFTiCaxKdgb/9OwUYAuyl8+XOc6rUbWq5zmHfvNLxruIzA54GXoxwM7Aowo/iWkgjgL641MrQKmUZMCPCa4ANuDTzJUBrxN8LzI9wX2AbrufVAAyM+Ebgm/jMRsXn0RRpq4u2zq13bHlHkGCuxz4R/4EfAlZJmlMj+0xJ24EdwMW4U5aCQkjsc/zHD3AVPqPFzNYBh0v5H5G0E/d5cB7e2QO04+JkAOOAA2a2z7wXWV7DrnI5u/DOEdwJykXAZrnU8GxgpLlExHrgBkkN+CBVTd20lo21mAKsNLN2M/sRaAEmRdoWM/vOzDrwgXcU8AtwDFgq6Wagp9pOqwAkDcIHj5aIfzPaoqDQP9oN7DH3C3Ac2E9X0byCP/B2Kc5pMbO2CI+K+OnAXdGenwFD6Ryon5XLNWzEZbmHxzkHzKw1wuXvR9IL+N+ojyb/DHMp3magWdJuvMNcVs4jaTTwBDDJzA5LWobP2AuOx76drt+tk3RM5NK61wGXm9nvctXH4lrHwp6a59eqRpU44foxt1VJWwU8hDty2WruMKWnNtaimjx1wfFSuB1oMLMTkiYD1+LiYg8D15yiDIDfepCnXGZHRfkdVP/9t8WA2+UcM+uIARO8jnPNrIvWU0wehgETzaxN0kE626uy7rk01IvIO4IESReqqxJqE/BthH/FBegABuId0BFJw3E3jadiE+50B0kzgMERPwg4HB3sOHzmXo2vgNGSxsRxtQ69spzx+PIQ+Ez+CknnR9pZksZGWjNwKXAPMcOuoDsb2+SyytXsmBXr6MPw2fmWGjYX+vyDzOx9/MF8U6281TCzI8BhSVdG1J34Xcjp5EPggaL+ksZK6o+3108xCEwDRp5mO5J/ibwjSMCfEbws/8vkCXxtt3h4uwT4QNL3ZjZN0g5gD760sLkH134KWBnLSS10+oxeD9wfywh78Q77JMzsWDykXSfpZ+BjYHyVrIuBN+J6rUTna2aHYqa6UlLfyDsf+NrM2uV/RZ2D3wFV0p2NS4Bdkrab2e2l+DW4OupO/A5lnpn9EANJNc4G3pV0Jj7TfqxGvu6YDbwq/5vvfuDuv3GNv8JSfGlnezzwPgTchDswWitpG/4Z/OdyzknPSPXRJEmSOieXhpIkSeqcHAiSJEnqnBwIkiRJ6pwcCJIkSeqcHAiSJEnqnBwIkiRJ6pwcCJIkSeqcPwGR69X/GibaRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "std = np.sqrt(var)\n",
    "\n",
    "xs = [-4, 4]\n",
    "fxs, fys = thinkstats2.FitLine(xs, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "thinkplot.PrePlot(2) \n",
    "xs, ys = thinkstats2.NormalProbability(weights)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "xs, ys = thinkstats2.NormalProbability(term_weights)\n",
    "thinkplot.Plot(xs, ys, label='full term')\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Birth weight (lbs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lognormal model\n",
    "\n",
    "As an example of a lognormal disrtribution, we'll look at adult weights from the BRFSS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brfss\n",
    "df = brfss.ReadBrfss()\n",
    "weights = df.wtkg2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function estimates the parameters of a normal distribution and plots the data and a normal model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNormalModel(weights):\n",
    "    \"\"\"Plots a CDF with a Normal model.\n",
    "\n",
    "    weights: sequence\n",
    "    \"\"\"\n",
    "    cdf = thinkstats2.Cdf(weights, label='weights')\n",
    "\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(weights)\n",
    "    std = np.sqrt(var)\n",
    "    print('n, mean, std', len(weights), mean, std)\n",
    "\n",
    "    xmin = mean - 4 * std\n",
    "    xmax = mean + 4 * std\n",
    "\n",
    "    xs, ps = thinkstats2.RenderNormalCdf(mean, std, xmin, xmax)\n",
    "    thinkplot.Plot(xs, ps, label='model', linewidth=4, color='0.8')\n",
    "    thinkplot.Cdf(cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of adult weights and a normal model, which is not a very good fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n, mean, std 398484 78.59599565702814 17.75455519179871\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalModel(weights)\n",
    "thinkplot.Config(title='Adult weight, linear scale', xlabel='Weight (kg)',\n",
    "                 ylabel='CDF', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of adult weight and a lognormal model, plotted on a log-x scale.  The model is a better fit for the data, although the heaviest people are heavier than the model expects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n, mean, std 398484 1.884660713731975 0.09623580259151371\n"
     ]
    },
    {
     "data": {
      "image/png": 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IlBh35913382606itPUl1dU26H4JKCP1v6tSpvPjiizQ0NFBVVcXBBx9MbW0tzz77LCeffDKvv/46hx9+OBAk7ZkzZ2a9/oUXXuDoo49ml112AeD000/nz3/+c3r7KaecQiwWY9KkSbz77rt5Y5g5cybf+ta3qKur49RTT2Wfffbp9+tUUhApMZkT43T2RaisrEr3VFbHtGhUVFQwfvx4fv7zn3PYYYcxefJknnrqKf76178yYcIEjjvuOO6+++5uX9/bOHOZQ3J3t+9ZZ53FjBkzWLJkCZ/+9Ke55ZZb0tVY/UVJQaSEtLa28sEHH6SXg2GvoaoqeCuXS6NyoRLfUUcdxfXXX8+tt97KAQccwOWXX87UqVM59NBD+cd//EfWrFnDxIkTaWpqoq6ujn333Tf92unTp3PZZZexceNGhg8fzv3335+erKc7w4cPzxrhdu3atey9995ccsklrF27lldffVVJQaRcuTvvvfde1rr2ZAc1NTWYqcpoIBx55JF861vfYubMmQwdOpTq6mqOPPJIxowZw2233cb8+fPTpbhrr702KynsvvvufPWrX2XGjBl89KMfZdKkSYwYMaLH802ePJlEIsGBBx7IueeeS0tLC7/4xS+oqKhgt912y2qz6C8aOlukRGzatIkNGzZkDYFdVVWVnulrsFcbDYahsxsbGxk2bBjJZJJ58+Zx/vnnM2/evH4/z44Mna1bUkVKQDKZpL6+Htg6BHY8nkgnhHKpNip111xzDVOmTOGTn/wkEyZM4JRTTil0SF2o+kikBNTX16eHsWhrD3ovV1ZWAqo2KiXXX399oUPolZKCSJFrbW3tMp1mZWUlsZgN+iqjXO6evu1W8tvRJgFVH4kUMXfn/fffz1oXi8VIJMqvuqi6upr6+vod/tAbzNyd+vp6qqurt/sYKimIFLGmpiaam5uBrX0SMudGKCfjxo2jrq4uqye3dFVdXc24ceO2+/VKCiJFqvNbX6egT4JTXRkHyq9xuaKiggkTJhQ6jEFP1UciRaqxsZG2tjYgKCW0tSepqFDjskRLJQWRIuTufPDBB1l9EhKJBPF4bFAPdCeFp5KCSBFqaGigvb09nRCA9KQ5KiFIlFRSECky7s7GjRuBrX0SEokENdVVmlJTIqekIFJktmzZQnt7e9a6igpVGcnAUPWRSBHJLCV0SiQSxGJ6q8rAUElBpIg0NTXR2tqabmCGrW0JIgMh0q8fZnaCmb1hZmvM7Mo82/c0s6fM7Pdm9qqZnRRlPCLF7sMPP+SJlWt48KnX0oPexWKxsuuTIIUTWVIwszhwM3AiMAmYb2aTcnb7T+CX7n4QcCbw46jiESl2ra2tNDc3p0sIABUVCfVJkAEVZfXRdGCNu68FMLN7gLnA6xn7OLBT+HwE8HaE8YgUtc5SQucdR7FYnPNOPUJ3G8mAirL6aHfgrYzlunBdpmuAz5tZHbAU+Eq+A5nZRWZWa2a1GvdEBqNkMslDj/+eB596Lb1u+NAaJQQZcFEmhXxDduUObzgfuM3dxwEnAf9tZl1icveF7j7N3aeNGTMmglBFCmvTpk1Z1UZmMeZ/ZkYBI5JyFWVSqAP2yFgeR9fqoQuAXwK4+/NANTA6wphEio67s3nz5nS1EcCCuTM4ZfaUAkYl5SrKpLAK2MfMJphZJUFD8uKcff4GzAYws08QJAXVD0lZaWxspKOjI2ONccZJhxYsHilvkSUFd08CFwOPAasJ7jJ6zcy+YWYnh7tdAVxoZq8AdwPnumbQkDKzadOmrGV1VpNCirTzmrsvJWhAzlx3Vcbz14HDo4xBpJi1trbS0tKSta6iQn1KpXD0dUSkgHJLCbFYXKUEKSj994kUSCqVorGxMWudSglSaEoKIgXS2NhIKpVKL5sZ8biSghSWkoJIgWzevDlrOZFIYPl694gMIH0tESmAzAbmzhFREwkNeieFp5KCSAE0NDQApEdETXZ4uoFZI6JKISkpiAwwd08nha1zJgSFdo2IKoWm6iORAdbU1JTuwRwMbWHE43EWzJ2pAfCk4FRSEBlgXRuY45iZEoIUBSUFkQHU0dFBU1MTELQnQHDXkUix0H+jyABqaGjg8RV/YcnyN2hrT2IWIxaLq3FZioZKCiIDqLGxMZ0QoLPqCDUuS9FQSUFkgLS1tdHS0pI1b8KwITWcOWe62hOkaCgpiAyQ3HGOYrE4d99wYYGiEclP1UciAyCzb0KnRCJeoGhEuqekIDIAWltbaW9vz1qnu46kGCkpiAyA3FJCPB70TRApNkoKIhFz93R7gvomSLFTUhCJWEtLCx0dHenB7yAoKahvghQjJQWRiDU0NGQlhGDeBFPfBClKSgoiEeqsOuocDRUgHk9o8DspWkoKIhFqamoilUpldFgzzjv1cCUEKVpKCiIRyu2wlkjEOWX2lAJFI9I7JQWRiLg7W7ZsyVqnu46k2CkpiESks+qo8zZUMyMWUy9mKW762iISkfseq+X+376cbk+Ix+NUV+k2VCluKimIRMDdeeDxl7NGRE0kEroNVYqekoJIBJqammht25oQqiorOHee7jqS4qfqI5EI5DYw/9c1ZzJ27NgCRSPSdyopiPSzzLGOOg0bNqxA0Yhsm0iTgpmdYGZvmNkaM7uym33+zsxeN7PXzOyuKOMRGQjNzc2kUqmMNUZNTU3B4hHZFpFVH5lZHLgZOA6oA1aZ2WJ3fz1jn32AfwcOd/eNZqbytZS8rn0TNEy2lI4oSwrTgTXuvtbd24B7gLk5+1wI3OzuGwHc/b0I4xGJXL6qo3hcTXdSOqJMCrsDb2Us14XrMu0L7Gtmz5nZCjM7Id+BzOwiM6s1s9oNGzZEFK7IjuscJnsrIx5XhzUpHVEmhXzlZc9ZTgD7ALOA+cAtZrZzlxe5L3T3ae4+bcyYMf0eqEh/ya06CmZYK1AwItshyqRQB+yRsTwOeDvPPg+7e7u7/w/wBkGSECk5+WdYUylBSkuUSWEVsI+ZTTCzSuBMYHHOPg8BxwCY2WiC6qS1EcYkEpm2tjaSyaRmWJOSFllScPckcDHwGLAa+KW7v2Zm3zCzk8PdHgPqzex14CngX9y9PqqYRKLU2NjYJSFohjUpNZHeFuHuS4GlOeuuynjuwOXhQ6SkbdmyRTOsSclTj2aRftDW1hY8MgbAO+/Uw5QQpOQoKYj0g9y7jmKxOPM+dXCBohHZfkoKIv0gXy9mkVKkpCCyg5LJJC0tLVnr1ItZSpX+c0V20JYtW3hi5Zp0I3MsFiMWU481KU0qKYjsoMbGRpYsfyNj2s2E+iZIyVJSENkBHR0dPPL0H7LuOho6pFp9E6Rk9ZgUzOy2jOfnRB6NSIlpamrK6ptQXVXBPTdcqFtRpWT1VlLI/M++NMpARErRli1bskoJpx2v21CltPWWFHJHNRWRUCqV6nIr6mmfVrWRlLbe7j4aZ2Y3EQyD3fk8zd0viSwykSLX3NxMMFJLwMyoqqoqYEQiO663pPAvGc9rowxEpNR0nTshoWk3peT1mBTc/faBCkSklLi7ejHLoNTrLalmdo6ZvWRmW8JHrZktGIjgRIpVvmk3YzElBSl9PZYUwg//fyIY2volgraFg4Hvmxnufkf0IYoUn9xezJp2UwaL3koKXwbmuftT7r7J3T909yeBz4XbRMpOZ9VRZi/mREIzrMng0FtD807uvi53pbuvM7OdoglJpLg98NsXuf3B57J7MdeoF7MMDr0lhebt3CYyaN29ZFVWQhhSXcVd13+xgBGJ9J/eksInzOzVPOsN2DuCeESKXlPz1mGyKysSnH6CejHL4NFbUjgQ2BV4K2f9XsDbkUQkUsSSySSpVCq9fOMVcxg/fnzhAhLpZ701NP8A2Ozub2Y+gKZwm0hZaWxszFqurq4mkdC0JDJ49JYUxrt7l+ojd68FxkcSkUgRy+2wNnTo0AJFIhKN3pJCdQ/bavozEJFi19HRQXNz9v0VSgoy2PSWFFaZ2YW5K83sAuDFaEISKU73PvoCl9+wJL0ci8WorKwsYEQi/a+3ytB/Ah40s7PZmgSmAZXAvCgDEykmDz/5Cv/9q5VZQ1sMqdGIqDL49DYg3rvAYWZ2DPDJcPWSsFezSNlYtHRVVkKorEhw5omHFDAikWj06bYJd38KeCriWESK1paMvgnzjtmfTx++H3vttVcBIxKJRq+jpIoIWaWE2TMmMnToUM2dIIOSkoJIL9ydZLIja92wYcMKFI1ItJQURHoR3Ia6ddrNeDxOdXVPd2uLlC4lBZFe5OuwpqojGawiTQpmdoKZvWFma8zsyh72O83M3Mw09rAUFXfvMrSFOqzJYBZZUjCzOHAzcCIwCZhvZpPy7DccuARYGVUsItsr37SbNTXqzC+DV5QlhenAGndf6+5twD3A3Dz7fRP4HtCSZ5tIQeVWHcXjcWIx1brK4BXlf/fuZA+5XReuSzOzg4A93P2Rng5kZheZWa2Z1W7YsKH/IxXJw9158PHfZw1tkUjECxiRSPSiTAr5WuLSt3CYWYxg+O0rejuQuy9092nuPm3MmDH9GKJI91pbW/nVM693mXZTZDCLMinUAXtkLI8je2Ke4QRDZzxtZuuAQ4HFamyWYnHfY7XZ027WVHHGSRraQga3KGcHWQXsY2YTgP8FzgTO6tzo7puA0Z3LZvY08M/hXA0iBfXQEy9z5yOr0suVFQluv+4chg8fXsCoRKIXWUnB3ZPAxcBjwGrgl+7+mpl9w8xOjuq8Iv3hnqUv4L512s05R+7HkCFDChiRyMCIdB5Bd18KLM1Zd1U3+86KMhaRbbGlKXsAvM/OOoB4XI3MMvjp3jqRHLljHc2eMVFjHUnZUFIQydHW1pZVdWRm6sUsZSPS6iORUvPwk6/w3w8/l7WupqZGVUdSNpQUREIPP/kKtz/0PM3Nrel1lRUJVR1JWVH1kUho0aO1pFKpdNVRZUWCOUfup6ojKSsqKYiEWtva6egIOqvNO2Z/Zs+YyJAhQ1R1JGVFJQWRkDskk0FSmD1jIoA6q0nZUVIQCaVSHbhvnWFNdx1JOVJSEAllz5sAQ4YM0TDZUnb0Hy9CZ4e1ZNY6VR1JOVJSEAGam5u7VB1prCMpR0oKItBlHuZhw4ap6kjKkv7rpeylUqm8SUGkHCkpSNlramrit8//Ob2sqiMpZ0oKUvbue2wVDz71Wnp5SE01ZvlmkxUZ/JQUpKw9+PhL3PPoS1nr5mvKTSljSgpSth5+8hV+/kD2iKinHTeZz31a04RL+VJSkLK16NHarL4J847Zn1OPm6qqIylrSgpStppbWkmlghFROwfAU4c1KXdKClK2MksJs2dMpKamhoqKigJGJFJ4SgpSlvINa7HTTjsVKBqR4qH5FKTsPPzkK9z1qxVZw1rEYjGNiCqCSgpShhY9WktjU3N6uXPKTQ1rIaKSgpSRh598hUWP1tLc0pYeJruyIsGcIz6uqiORkJKClI1Fj9bS2tZOMtkOBAnhxivmUFlZSVVVVYGjEykOKi9L2Whta09PudlZQgAYMWKE+iaIhFRSkLLS0RFMuXnjFXOAYPA7jYgqspVKClJWOquOOg0bNox4PF6gaESKj5KClI1UKtVlHuYRI0YUKBqR4qSkIGUjt7NaVVUV1dXVBYpGpDhFmhTM7AQze8PM1pjZlXm2X25mr5vZq2b2hJntFWU8Ur5SqRTt7dlJQaUEka4iSwpmFgduBk4EJgHzzWxSzm6/B6a5+2TgPuB7UcUj5W3R0hVAdg9mNTCLdBVlSWE6sMbd17p7G3APMDdzB3d/yt2bwsUVwLgI45Ey9dATL3PHwyvTy5UVCUaMGKEezCJ5RPmu2B14K2O5LlzXnQuAR/NtMLOLzKzWzGo3bNjQjyFKObj7kZW4p9LLc47cT1VHIt2Isp9Cvt5AnmcdZvZ5YBpwdL7t7r4QWAgwbdq0vMcQydU5rEXDlq3jHM07Zn9OmT2FREJddETyifKdUQfskbE8Dng7dycz+xTwH8DR7t4aYTxSZhY9WktTcwup1NZxjmbPmMjOO+9c4MhEileU1UergH3MbIKZVQJnAoszdzCzg4CfAie7+3sRxiJlqLWtnfb2reMczTni49TU1GicI5EeRFZScPekmV0MPAbEgVvd/TUz+wbaCHHUAAANkklEQVRQ6+6Lge8Dw4B7w7Fn/ubuJ0cVk5SXjo6tndU6h7UYOXJkIUMSKXqRVqy6+1Jgac66qzKefyrK80t56ywldKqurqampqZA0YiUBt2TJ4PSfY/V0tGR3Vlt5MiRGg1VpBe6BUMGlc47jjZtbkivq6xIUFlZyZAhQwoYmUhpUElBBpXOO44yB76bc8THGTVqlEoJIn2gkoIMGg8/+UreO45OOmp/lRJE+khJQQaNRY/W0tHRQUdHR3qqTUClBJFtoOojGTRaWttpa2sDSE+1WVNTozuORLaBkoIMGslkklQqGONo9oyJgEoJIttKSUEGhY6ODtrb27LWDR8+XJPoiGwjJQUZFO5a/BzuW8dKNDN22WWXAkYkUpqUFKTk3ffrVdy5ZFV6ubIiwciRI6moqChgVCKlSUlBStpDT7zMrQ8sz1p38qxJGglVZDvpllQpSem5Ehq3pBuXIZgv4azPHq5Z1US2k945UpI6ey5nDno375j9OeVTB6mjmsgOUElBSlJLaxutrcHdRp09l48/7OOMHj26wJGJlDYlBSk57k5ra1t63uXOnstjx44lHo8XMjSRkqfqIyk59yx5vsuw2DvttBNDhw4tUEQig4eSgpSUe3/9Arc/9Hx6uXNYbFUbifQPVR9JSXj4yVe4Z8kLbNzUkLV+zpH7sdtuu+luI5F+oneSlIR7lr7Ah5sbgK29lucdsz9nn3w4lZWVhQtMZJBRSUGKXkdHB5s2N6aHsei82+j0E6czbNiwAkcnMrgoKUhRSyaTvP3221kd1G68Yg7Dhw9n5MiRBYxMZHBSUpCi1d7ezu0PPM3ip1/PWj906FDGjh2rIbFFIqCkIEWpubmZXzz0LPc/8Yes9TXVVey6665KCCIRUVKQouLu3P3I8yxauorW9uy+CEOqq1hwisY1EomSkoIUjWQyyXvvvcc9S1fRlpMQ5p84lQWnHq0SgkjElBSk4NydzZs3s2jpCh5Z9qeshFBZkeCME6dy1mcPV0IQGQBKClIw7s4vH13J3UtW0dLa1mV7VWWCO793gW47FRlASgoy4FKpFI2NjXz44Yfc+auVXaqKAGqqK/nCyYcpIYgMMCUFGRDuTktLC42NjTQ0NPDb5//MkuVvdEkIVRUJzjjpEOZ/Zqaqi0QKQElBIuHutLe309LSQlNTE01NTd0mAgjaDv7rmjMZNWoUiYT+LUUKRe8+2SHuTjKZpL29Pf1obW2ltbU13Qv5iZVruk0GAENqqjnrMzPYddddBzJ0Eckj0qRgZicAPwTiwC3u/p2c7VXAHcBUoB44w93XRRlTqevo6EiPAdQXuft2Luf7me+RSqXSP1OpFB0dHV0e+fSWCMyMoWEyOPX4qX2+HhGJVmRJwcziwM3AcUAdsMrMFrt75pgFFwAb3X2imZ0JfBc4I6qYBoO6urqseYkHSm8f8n1jDK2p4owTp3H6idPVZiBShKIsKUwH1rj7WgAzuweYC2QmhbnANeHz+4AfmZn5tnwV7qPPXfqT/j5kQTQ1NaenoSx+RiwWIx6PMaSmmvlzpnPK7CmFDkpEehBlUtgdeCtjuQ6Y0d0+7p40s03AKOD9zJ3M7CLgIoA999wzqnhlO5lZ+IgRiwWJIBaLUVNdyRknHsLcYw8sdIgi0kdRJoV8dQO5JYC+7IO7LwQWAkybNq3fSxGlJKhx2d5ql62vy19zYxnrLX2uznXVVRV87riD+OysA4jH48TjcRKJhKqBRAaRKJNCHbBHxvI44O1u9qkzswQwAvggimDu/+GXojisiMigEuVwk6uAfcxsgplVAmcCi3P2WQycEz4/DXgyivYEERHpm8hKCmEbwcXAYwS3pN7q7q+Z2TeAWndfDPwM+G8zW0NQQjgzqnhERKR3kfZTcPelwNKcdVdlPG8BTo8yBhER6TvNViIiImlKCiIikqakICIiaUoKIiKSZqV2B6iZbQDeLHQcvRhNTq/sEqZrKU66luJVrNezl7uP6W2nkksKpcDMat19WqHj6A+6luKkaylepX49qj4SEZE0JQUREUlTUojGwkIH0I90LcVJ11K8Svp61KYgIiJpKimIiEiakoKIiKQpKWwnM7vVzN4zsz/2st8hZtZhZqcNVGzbqi/XYmazzOxlM3vNzJ4ZyPi2RW/XYmYjzOxXZvZKeC3nDXSMfWVme5jZU2a2Ooz10jz7mJndZGZrzOxVMzu4ELH2po/XcnZ4Da+a2e/MrCin7OvLtWTsW/Tv/y7cXY/teABHAQcDf+xhnzjwJMFIsacVOubtvRZgZ4K5tfcMl8cWOuYduJavAt8Nn48hGLK9stBxdxPrR4CDw+fDgT8Dk3L2OQl4lGBavUOBlYWOeweu5TBgZPj8xFK+lnBbSbz/cx8qKWwnd19G77PEfQW4H3gv+oi2Xx+u5SzgAXf/W7h/0V5PH67FgeEWzCE6LNw3ORCxbSt3X+/uL4XPG4DVBPOaZ5oL3OGBFcDOZvaRAQ61V325Fnf/nbtvDBdXEMzWWHT6+HeBEnn/51JSiIiZ7Q7MA35S6Fj6wb7ASDN72sxeNLMFhQ5oB/wI+ATB1LB/AC5191RhQ+qdmY0HDgJW5mzaHXgrY7mO/B9QRaOHa8l0AUEJqKh1dy2l/P6PdJKdMvd/gH9z945BMLF9ApgKzAZqgOfNbIW7/7mwYW2XTwMvA8cCHwN+a2bPuvvmwobVPTMbRvCN85/yxJnvn6to7zPv5Vo69zmGICkcMZCxbaterqVk3/9KCtGZBtwT/kOMBk4ys6S7P1TYsLZLHfC+u28BtpjZMuBAgrrUUnMe8B0PKn3XmNn/APsBLxQ2rPzMrILgg+dOd38gzy51wB4Zy+MISkFFpw/XgplNBm4BTnT3+oGMb1v04VpK9v2v6qOIuPsEdx/v7uOB+4Avl8I/RDceBo40s4SZDQFmENSjlqK/EZR4MLNdgY8DawsaUTfCdo+fAavd/cZudlsMLAjvQjoU2OTu6wcsyD7qy7WY2Z7AA8AXirkU2pdrKeX3v0oK28nM7gZmAaPNrA64GqgAcPeSqkfs7VrcfbWZ/Rp4FUgBt7h7j7fiFkof/i7fBG4zsz8QVL38m7sX4zDHAIcDXwD+YGYvh+u+CuwJ6etZSnAH0hqgiaAkVIz6ci1XAaOAH4ffsJNenKON9uVaSpaGuRARkTRVH4mISJqSgoiIpCkpiIhImpKCiIikKSmIiEiakoIULTP7gZn9U8byY2Z2S8byDWZ2eS/H+F0fzrPOzEbnWT/LzA7r4XWnmNlV4fNrzOyfeztXN8cZFY662WhmP8rZNtXM/hCOgnqT5ekea2a3bcsonGb2GTP7+vbEKoOfkoIUs98RjJyJmcUIeobun7H9MOC5ng7g7t1+qPfBrM7zd+NfgR/vwPE7tQBfA/Illf8HXATsEz5O6IfzLQFODjsiimRRUpBi9hxbP5T3B/4INJjZSDOrIhjY7vcAZvYvZrYqHIs//S3YzBrDnzEz+3E4/v0jZrY059v1V8zspfBb+X7hQGdfAi6zYB6JIzMDM7N9gdZ8Hd/MbIqZrQhjedDMRobrDwnXPW9m37dwzgd33+LuywmSQ+ZxPgLs5O7Ph8Ny3AGc0tMvzMy+GZYcYmZ2kpn9ycyWh6WMR8LzOfA08JmejiXlSUlBipa7vw0kw+EPDgOeJxiNcibB2DKvunubmR1P8C16OjAFmGpmR+Uc7lRgPHAA8MXwGJned/eDCb6Z/7O7ryMY4fIH7j7F3Z/N2f9w4KVuQr+DoKf0ZIKRWK8O1/8c+JK7zwQ6+vAr2J1gbKNOPY6AambfA8YS9GquBH5KMIbQEQRzR2SqBY5EJIeSghS7ztJCZ1J4PmO5s73g+PDxe4IP6v0IkkSmI4B73T3l7u8AT+Vs7xzU7EWC5NGbjwAbclea2QhgZ3fvnJ3uduAoM9sZGO7unTHf1YdzbMsIqF8Lz/v3YUlgP2Ctu/9PuP3unP3fAz7ahxikzGjsIyl2ne0KBxBUH70FXAFsBm4N9zHg2+7+0x6O09v4xa3hzw769r5oBkb0Yb++nj+fOrInmulpBNRVBCWkXdz9gz6cr5rgGkSyqKQgxe45grrvD9y9I/zA25mg+uf5cJ/HgPPD8e0xs93NbGzOcZYDnwvr2nclaETuTQPBdIv5rAYm5q50903Axow2iC8Az4QzijWEI5kCnNnbycPRThvM7NDwrqMFBCPW5vNr4DvAEjMbDvwJ2DtsGwE4I2f/fQmSrEgWJQUpdn8guOtoRc66TZ2NvO7+G4LqmOfD0U/vo+uH+f0E37z/SFDXvhLY1Mu5fwXMy9fQDCwDDsp3iyhwDvB9M3uVoI3jG+H6C4CFZvY8wTf59PnNbB1wI3CumdWZ2aRw0z8QzC+wBvgrPcxG5u73Av9FMJw2wJeBX5vZcuDdnOs9huAuJJEsGiVVyoaZDXP3RjMbRTCpzuFh+8L2Hu+HwK/c/fFtOX/4/ErgI+5+6faev6/nCxPXzcBf3P0HYUnpLnefHdW5pXSpTUHKySNhg28l8M0dSQih6wgmHOqrOWb27wTvuzeBc3fw/L250MzOIbje3xOUkCAY9/+KiM8tJUolBRERSVObgoiIpCkpiIhImpKCiIikKSmIiEiakoKIiKT9fxZBlLgSiF2HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_weights = np.log10(weights)\n",
    "MakeNormalModel(log_weights)\n",
    "thinkplot.Config(title='Adult weight, log scale', xlabel='Weight (log10 kg)',\n",
    "                 ylabel='CDF', loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function generates a normal probability plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeNormalPlot(weights):\n",
    "    \"\"\"Generates a normal probability plot of birth weights.\n",
    "\n",
    "    weights: sequence\n",
    "    \"\"\"\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "    std = np.sqrt(var)\n",
    "\n",
    "    xs = [-5, 5]\n",
    "    xs, ys = thinkstats2.FitLine(xs, mean, std)\n",
    "    thinkplot.Plot(xs, ys, color='0.8', label='model')\n",
    "\n",
    "    xs, ys = thinkstats2.NormalProbability(weights)\n",
    "    thinkplot.Plot(xs, ys, label='weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we generate a normal probability plot with adult weights, we can see clearly that the data deviate from the model systematically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalPlot(weights)\n",
    "thinkplot.Config(title='Adult weight, normal plot', xlabel='Weight (kg)',\n",
    "                 ylabel='CDF', loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we make a normal probability plot with log weights, the model fit the data well except in the tails, where the heaviest people exceed expectations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalPlot(log_weights)\n",
    "thinkplot.Config(title='Adult weight, lognormal plot', xlabel='Weight (log10 kg)',\n",
    "                 ylabel='CDF', loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pareto distribution\n",
    "\n",
    "Here's what the Pareto CDF looks like with a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin = 0.5\n",
    "\n",
    "thinkplot.PrePlot(3)\n",
    "for alpha in [2.0, 1.0, 0.5]:\n",
    "    xs, ps = thinkstats2.RenderParetoCdf(xmin, alpha, 0, 10.0, n=100) \n",
    "    thinkplot.Plot(xs, ps, label=r'$\\alpha=%g$' % alpha)\n",
    "    \n",
    "thinkplot.Config(title='Pareto CDF', xlabel='x',\n",
    "                 ylabel='CDF', loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of populations for cities and towns is sometimes said to be Pareto-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cities/towns 19515\n"
     ]
    }
   ],
   "source": [
    "import populations\n",
    "\n",
    "pops = populations.ReadData()\n",
    "print('Number of cities/towns', len(pops))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution of population for cities and towns in the U.S., along with a Pareto model.  The model fits the data well in the tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_pops = np.log10(pops)\n",
    "cdf = thinkstats2.Cdf(pops, label='data')\n",
    "cdf_log = thinkstats2.Cdf(log_pops, label='data')\n",
    "\n",
    "# pareto plot\n",
    "xs, ys = thinkstats2.RenderParetoCdf(xmin=5000, alpha=1.4, low=0, high=1e7)\n",
    "thinkplot.Plot(np.log10(xs), 1-ys, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf_log, complement=True) \n",
    "thinkplot.Config(xlabel='log10 population',\n",
    "                 ylabel='CCDF',\n",
    "                 yscale='log', loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lognormal model might be a better fit for this data (as is often the case for things that are supposed to be Pareto)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(cols=2)\n",
    "\n",
    "mu, sigma = log_pops.mean(), log_pops.std()\n",
    "xs, ps = thinkstats2.RenderNormalCdf(mu, sigma, low=0, high=8)\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf_log) \n",
    "thinkplot.Config(xlabel='log10 population',\n",
    "                 ylabel='CDF', loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a normal probability plot for the log-populations.  The model fits the data well except in the right tail, where the biggest cities are bigger than expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkstats2.NormalProbabilityPlot(log_pops, label='data')\n",
    "thinkplot.Config(xlabel='Random variate',\n",
    "                 ylabel='log10 population',\n",
    "                 xlim=[-5, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random variates\n",
    "\n",
    "When we have an analytic CDF, we can sometimes invert it to generate random values.  The following function generates values from an exponential distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def expovariate(lam):\n",
    "    p = random.random()\n",
    "    x = -np.log(1-p) / lam\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test it by generating a sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = [expovariate(lam=2) for _ in range(1000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plotting the CCDF on a log-y scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf = thinkstats2.Cdf(t)\n",
    "\n",
    "thinkplot.Cdf(cdf, complement=True)\n",
    "thinkplot.Config(xlabel='Exponential variate', ylabel='CCDF', yscale='log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A straight line is consistent with an exponential distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As an exercise, write a function that generates a Pareto variate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** In the BRFSS (see Section 5.4), the distribution of heights is roughly normal with parameters µ = 178 cm and σ = 7.7 cm for men, and µ = 163 cm and σ = 7.3 cm for women.\n",
    "\n",
    "In order to join Blue Man Group, you have to be male between 5’10” and 6’1” (see http://bluemancasting.com). What percentage of the U.S. male population is in this range? Hint: use `scipy.stats.norm.cdf`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`scipy.stats` contains objects that represent analytic distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example <tt>scipy.stats.norm</tt> represents a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.stats._distn_infrastructure.rv_frozen"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = 178\n",
    "sigma = 7.7\n",
    "dist = scipy.stats.norm(loc=mu, scale=sigma)\n",
    "type(dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A \"frozen random variable\" can compute its mean and standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178.0, 7.7)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist.mean(), dist.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also evaluate its CDF.  How many people are more than one standard deviation below the mean?  About 16%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1586552539314574"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist.cdf(mu-sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many people are between 5'10\" and 6'1\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** To get a feel for the Pareto distribution, let’s see how different the world would be if the distribution of human height were Pareto. With the parameters xm = 1 m and α = 1.7, we get a distribution with a reasonable minimum, 1 m, and median, 1.5 m.\n",
    "\n",
    "Plot this distribution. What is the mean human height in Pareto world? What fraction of the population is shorter than the mean? If there are 7 billion people in Pareto world, how many do we expect to be taller than 1 km? How tall do we expect the tallest person to be?\n",
    "\n",
    "`scipy.stats.pareto` represents a pareto distribution.  In Pareto world, the distribution of human heights has parameters alpha=1.7 and xmin=1 meter.  So the shortest person is 100 cm and the median is 150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5034066538560549"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = 1.7\n",
    "xmin = 1       # meter\n",
    "dist = scipy.stats.pareto(b=alpha, scale=xmin)\n",
    "dist.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the mean height in Pareto world?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What fraction of people are shorter than the mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of 7 billion people, how many do we expect to be taller than 1 km?  You could use <tt>dist.cdf</tt> or <tt>dist.sf</tt>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How tall do we expect the tallest person to be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The Weibull distribution is a generalization of the exponential distribution that comes up in failure analysis (see http://wikipedia.org/wiki/Weibull_distribution). Its CDF is\n",
    "\n",
    "$\\mathrm{CDF}(x) = 1 − \\exp[−(x / λ)^k]$ \n",
    "\n",
    "Can you find a transformation that makes a Weibull distribution look like a straight line? What do the slope and intercept of the line indicate?\n",
    "\n",
    "Use `random.weibullvariate` to generate a sample from a Weibull distribution and use it to test your transformation.\n",
    "\n",
    "Generate a sample from a Weibull distribution and plot it using a transform that makes a Weibull distribution look like a straight line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`thinkplot.Cdf` provides a transform that makes the CDF of a Weibull distribution look like a straight line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = [random.weibullvariate(2, 1) for _ in range(1000)]\n",
    "cdf = thinkstats2.Cdf(sample)\n",
    "thinkplot.Cdf(cdf, transform='weibull')\n",
    "thinkplot.Config(xlabel='Weibull variate', ylabel='CCDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** For small values of `n`, we don’t expect an empirical distribution to fit an analytic distribution exactly. One way to evaluate the quality of fit is to generate a sample from an analytic distribution and see how well it matches the data.\n",
    "\n",
    "For example, in Section 5.1 we plotted the distribution of time between births and saw that it is approximately exponential. But the distribution is based on only 44 data points. To see whether the data might have come from an exponential distribution, generate 44 values from an exponential distribution with the same mean as the data, about 33 minutes between births.\n",
    "\n",
    "Plot the distribution of the random values and compare it to the actual distribution. You can use random.expovariate to generate the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32.72727272727273, 28.877692375846223)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import analytic\n",
    "\n",
    "df = analytic.ReadBabyBoom()\n",
    "diffs = df.minutes.diff()\n",
    "cdf = thinkstats2.Cdf(diffs, label='actual')\n",
    "\n",
    "n = len(diffs)\n",
    "lam = 44.0 / 24 / 60\n",
    "sample = [random.expovariate(lam) for _ in range(n)]\n",
    "\n",
    "1/lam, np.mean(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Worked Example:** The distributions of wealth and income are sometimes modeled using lognormal and Pareto distributions. To see which is better, let’s look at some data.\n",
    "\n",
    "The Current Population Survey (CPS) is a joint effort of the Bureau of Labor Statistics and the Census Bureau to study income and related variables. Data collected in 2013 is available from http://www.census.gov/hhes/www/cpstables/032013/hhinc/toc.htm. I downloaded `hinc06.xls`, which is an Excel spreadsheet with information about household income, and converted it to `hinc06.csv`, a CSV file you will find in the repository for this book. You will also find `hinc.py`, which reads this file.\n",
    "\n",
    "Extract the distribution of incomes from this dataset. Are any of the analytic distributions in this chapter a good model of the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>freq</th>\n",
       "      <th>cumsum</th>\n",
       "      <th>ps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.999000e+03</td>\n",
       "      <td>4204</td>\n",
       "      <td>4204</td>\n",
       "      <td>0.034330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.999000e+03</td>\n",
       "      <td>4729</td>\n",
       "      <td>8933</td>\n",
       "      <td>0.072947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.499900e+04</td>\n",
       "      <td>6982</td>\n",
       "      <td>15915</td>\n",
       "      <td>0.129963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.999900e+04</td>\n",
       "      <td>7157</td>\n",
       "      <td>23072</td>\n",
       "      <td>0.188407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.499900e+04</td>\n",
       "      <td>7131</td>\n",
       "      <td>30203</td>\n",
       "      <td>0.246640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.999900e+04</td>\n",
       "      <td>6740</td>\n",
       "      <td>36943</td>\n",
       "      <td>0.301679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.499900e+04</td>\n",
       "      <td>6354</td>\n",
       "      <td>43297</td>\n",
       "      <td>0.353566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.999900e+04</td>\n",
       "      <td>5832</td>\n",
       "      <td>49129</td>\n",
       "      <td>0.401191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.499900e+04</td>\n",
       "      <td>5547</td>\n",
       "      <td>54676</td>\n",
       "      <td>0.446488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.999900e+04</td>\n",
       "      <td>5254</td>\n",
       "      <td>59930</td>\n",
       "      <td>0.489392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.499900e+04</td>\n",
       "      <td>5102</td>\n",
       "      <td>65032</td>\n",
       "      <td>0.531056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.999900e+04</td>\n",
       "      <td>4256</td>\n",
       "      <td>69288</td>\n",
       "      <td>0.565810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.499900e+04</td>\n",
       "      <td>4356</td>\n",
       "      <td>73644</td>\n",
       "      <td>0.601382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.999900e+04</td>\n",
       "      <td>3949</td>\n",
       "      <td>77593</td>\n",
       "      <td>0.633629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.499900e+04</td>\n",
       "      <td>3756</td>\n",
       "      <td>81349</td>\n",
       "      <td>0.664301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.999900e+04</td>\n",
       "      <td>3414</td>\n",
       "      <td>84763</td>\n",
       "      <td>0.692180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.499900e+04</td>\n",
       "      <td>3326</td>\n",
       "      <td>88089</td>\n",
       "      <td>0.719341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.999900e+04</td>\n",
       "      <td>2643</td>\n",
       "      <td>90732</td>\n",
       "      <td>0.740923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.499900e+04</td>\n",
       "      <td>2678</td>\n",
       "      <td>93410</td>\n",
       "      <td>0.762792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9.999900e+04</td>\n",
       "      <td>2223</td>\n",
       "      <td>95633</td>\n",
       "      <td>0.780945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.049990e+05</td>\n",
       "      <td>2606</td>\n",
       "      <td>98239</td>\n",
       "      <td>0.802226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.099990e+05</td>\n",
       "      <td>1838</td>\n",
       "      <td>100077</td>\n",
       "      <td>0.817235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.149990e+05</td>\n",
       "      <td>1986</td>\n",
       "      <td>102063</td>\n",
       "      <td>0.833453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.199990e+05</td>\n",
       "      <td>1464</td>\n",
       "      <td>103527</td>\n",
       "      <td>0.845408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.249990e+05</td>\n",
       "      <td>1596</td>\n",
       "      <td>105123</td>\n",
       "      <td>0.858441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.299990e+05</td>\n",
       "      <td>1327</td>\n",
       "      <td>106450</td>\n",
       "      <td>0.869278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.349990e+05</td>\n",
       "      <td>1253</td>\n",
       "      <td>107703</td>\n",
       "      <td>0.879510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.399990e+05</td>\n",
       "      <td>1140</td>\n",
       "      <td>108843</td>\n",
       "      <td>0.888819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.449990e+05</td>\n",
       "      <td>1119</td>\n",
       "      <td>109962</td>\n",
       "      <td>0.897957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.499990e+05</td>\n",
       "      <td>920</td>\n",
       "      <td>110882</td>\n",
       "      <td>0.905470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.549990e+05</td>\n",
       "      <td>1143</td>\n",
       "      <td>112025</td>\n",
       "      <td>0.914803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1.599990e+05</td>\n",
       "      <td>805</td>\n",
       "      <td>112830</td>\n",
       "      <td>0.921377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.649990e+05</td>\n",
       "      <td>731</td>\n",
       "      <td>113561</td>\n",
       "      <td>0.927347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.699990e+05</td>\n",
       "      <td>575</td>\n",
       "      <td>114136</td>\n",
       "      <td>0.932042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.749990e+05</td>\n",
       "      <td>616</td>\n",
       "      <td>114752</td>\n",
       "      <td>0.937072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.799990e+05</td>\n",
       "      <td>570</td>\n",
       "      <td>115322</td>\n",
       "      <td>0.941727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.849990e+05</td>\n",
       "      <td>502</td>\n",
       "      <td>115824</td>\n",
       "      <td>0.945826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.899990e+05</td>\n",
       "      <td>364</td>\n",
       "      <td>116188</td>\n",
       "      <td>0.948799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1.949990e+05</td>\n",
       "      <td>432</td>\n",
       "      <td>116620</td>\n",
       "      <td>0.952327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.999990e+05</td>\n",
       "      <td>378</td>\n",
       "      <td>116998</td>\n",
       "      <td>0.955413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.499990e+05</td>\n",
       "      <td>2549</td>\n",
       "      <td>119547</td>\n",
       "      <td>0.976229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>inf</td>\n",
       "      <td>2911</td>\n",
       "      <td>122458</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          income  freq  cumsum        ps\n",
       "0   4.999000e+03  4204    4204  0.034330\n",
       "1   9.999000e+03  4729    8933  0.072947\n",
       "2   1.499900e+04  6982   15915  0.129963\n",
       "3   1.999900e+04  7157   23072  0.188407\n",
       "4   2.499900e+04  7131   30203  0.246640\n",
       "5   2.999900e+04  6740   36943  0.301679\n",
       "6   3.499900e+04  6354   43297  0.353566\n",
       "7   3.999900e+04  5832   49129  0.401191\n",
       "8   4.499900e+04  5547   54676  0.446488\n",
       "9   4.999900e+04  5254   59930  0.489392\n",
       "10  5.499900e+04  5102   65032  0.531056\n",
       "11  5.999900e+04  4256   69288  0.565810\n",
       "12  6.499900e+04  4356   73644  0.601382\n",
       "13  6.999900e+04  3949   77593  0.633629\n",
       "14  7.499900e+04  3756   81349  0.664301\n",
       "15  7.999900e+04  3414   84763  0.692180\n",
       "16  8.499900e+04  3326   88089  0.719341\n",
       "17  8.999900e+04  2643   90732  0.740923\n",
       "18  9.499900e+04  2678   93410  0.762792\n",
       "19  9.999900e+04  2223   95633  0.780945\n",
       "20  1.049990e+05  2606   98239  0.802226\n",
       "21  1.099990e+05  1838  100077  0.817235\n",
       "22  1.149990e+05  1986  102063  0.833453\n",
       "23  1.199990e+05  1464  103527  0.845408\n",
       "24  1.249990e+05  1596  105123  0.858441\n",
       "25  1.299990e+05  1327  106450  0.869278\n",
       "26  1.349990e+05  1253  107703  0.879510\n",
       "27  1.399990e+05  1140  108843  0.888819\n",
       "28  1.449990e+05  1119  109962  0.897957\n",
       "29  1.499990e+05   920  110882  0.905470\n",
       "30  1.549990e+05  1143  112025  0.914803\n",
       "31  1.599990e+05   805  112830  0.921377\n",
       "32  1.649990e+05   731  113561  0.927347\n",
       "33  1.699990e+05   575  114136  0.932042\n",
       "34  1.749990e+05   616  114752  0.937072\n",
       "35  1.799990e+05   570  115322  0.941727\n",
       "36  1.849990e+05   502  115824  0.945826\n",
       "37  1.899990e+05   364  116188  0.948799\n",
       "38  1.949990e+05   432  116620  0.952327\n",
       "39  1.999990e+05   378  116998  0.955413\n",
       "40  2.499990e+05  2549  119547  0.976229\n",
       "41           inf  2911  122458  1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hinc\n",
    "df = hinc.ReadData()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the CDF looks like on a linear scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ps = df.income.values, df.ps.values\n",
    "cdf = thinkstats2.Cdf(xs, ps, label='data')\n",
    "cdf_log = thinkstats2.Cdf(np.log10(xs), ps, label='data')\n",
    "    \n",
    "# linear plot\n",
    "thinkplot.Cdf(cdf) \n",
    "thinkplot.Config(xlabel='household income',\n",
    "                   ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check whether a Pareto model describes the data well, I plot the CCDF on a log-log scale.\n",
    "\n",
    "I found parameters for the Pareto model that match the tail of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ys = thinkstats2.RenderParetoCdf(xmin=55000, alpha=2.5, \n",
    "                                     low=0, high=250000)\n",
    "\n",
    "thinkplot.Plot(xs, 1-ys, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf, complement=True) \n",
    "thinkplot.Config(xlabel='log10 household income',\n",
    "                 ylabel='CCDF',\n",
    "                 xscale='log',\n",
    "                 yscale='log', \n",
    "                 loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the lognormal model I estimate mu and sigma using percentile-based statistics (median and IQR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.740354793159152 0.35\n"
     ]
    }
   ],
   "source": [
    "median = cdf_log.Percentile(50)\n",
    "iqr = cdf_log.Percentile(75) - cdf_log.Percentile(25)\n",
    "std = iqr / 1.349\n",
    "\n",
    "# choose std to match the upper tail\n",
    "std = 0.35\n",
    "print(median, std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the distribution, and fitted model, look like on a log-x scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ps = thinkstats2.RenderNormalCdf(median, std, low=3.5, high=5.5)\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.8')\n",
    "\n",
    "thinkplot.Cdf(cdf_log) \n",
    "thinkplot.Config(xlabel='log10 household income',\n",
    "                 ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My conclusions based on these figures are:\n",
    "\n",
    "1) The Pareto model might be a reasonable choice for the top\n",
    "   10-20% of incomes.\n",
    "\n",
    "2) The lognormal model captures the shape of the distribution better,\n",
    "   with some deviation in the left tail.  With different\n",
    "   choices for sigma, you could match the upper or lower tail, but not\n",
    "   both at the same time.\n",
    " \n",
    "In summary I would say that neither model captures the whole distribution,\n",
    "so you might have to \n",
    "\n",
    "1) look for another analytic model, \n",
    "\n",
    "2) choose one that captures the part of the distribution that is most \n",
    "   relevent, or \n",
    "\n",
    "3) avoid using an analytic model altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
